Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch
Quick Answer
Parallax introduces a learned projector to replace LLA's per-query solver, achieving double the arithmetic intensity and enhancing perplexity at 0.6B and 1.7B parameters.
Quick Take
This advancement significantly impacts model efficiency and performance in local linear attention mechanisms.
Key Points
- Parallax replaces LLA's per-query solver with a learned projector.
- Achieves double the arithmetic intensity compared to previous models.
- Improves perplexity metrics at 0.6B and 1.7B parameters.
- Enhances local linear attention mechanisms significantly.
- Targets improvements in model efficiency and performance.
Article Excerpt
From source RSS / original summaryParallax replaces LLA's per-query solver with a learned projector, doubling arithmetic intensity and improving perplexity at 0. 6B and 1. 7B. The post Parallax: A Parameterized Local Linear Attention That Keeps Softmax and Adds a Learned Covariance Correction Branch appeared first on MarkTechPost.
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